Methods, Systems, and Products for Targeting Advertisements

ABSTRACT

Methods, systems, and products are disclosed for targeting advertisements. Clickstream data is received that describes at least subscriber actions. Content information is received that describes what content is available. A criterion is also received for determining which subscribers should receive a targeted advertisement. The clickstream data is merged with the content information to generate data describing at least one event timeline. When the data describing the at least one event timeline matches the at least one criterion, then at least one subscriber is targeted to receive the advertisement.

CROSS REFERENCES

This application is a continuation of U.S. application Ser. No.11/636,068, filed Dec. 8, 2006 (Attorney Docket 01342 CON), and nowissued as U.S. Pat. No. X,XXX,XXX, which is itself a continuation ofU.S. application Ser. No. 10/017,640, filed Dec. 14, 2001 (AttorneyDocket 01342), and now issued as U.S. Pat. No. 7,212,979, with bothapplications incorporated herein by reference in their entirety.

This application relates to applicant's co-pending U.S. patentapplication Ser. No. 11/154,248 entitled “Method and System for TrackingNetwork Use,” (Attorney Docket 95003CON-2) filed on Jun. 16, 2005, andof which is incorporate herein by reference.

This application relates to applicant's co-pending U.S. patentapplication Ser. No. 10/017,742 entitled a “System and Method forIdentifying Desirable Subscribers,” (Attorney Docket 01341) filed onDec. 14, 2001, and of which is incorporated herein by this reference.

NOTICE OF COPYRIGHT PROTECTION

A portion of the disclosure of this patent document and its figurescontain material subject to copyright protection. The copyright ownerhas no objection to the facsimile reproduction by anyone of the patentdocument or the patent disclosure, but otherwise reserves all copyrightswhatsoever.

BACKGROUND

The exemplary embodiments generally relate to the measurement ofcontent-access patterns, and, more particularly, relate to utilizingcontent-access patterns and other subscriber-specific information toidentify desirable subscribers.

Content providers derive revenue directly or indirectly fromsubscribers. Therefore, content providers, including, for example,advertisers, content creators, and content distributors strive toidentify desirable subscribers. A subscriber may be desirable for anynumber of reasons. For example, a subscriber may be desirable due topast spending nor viewing patterns, which indicate a propensity forexhibiting a related behavior that the content provider considersdesirable. For example, the subscriber may also be desirable due to thesubscriber's demographic profile, including the subscriber's age,income, or other attribute. A desirable subscriber is a subscriber whois likely to make a decision to purchase a product or service or to viewa provider's content.

Content providers utilize various methods to identify desirablesubscribers, such as monitoring the subscriber's content-access patternsand performing surveys to determine a subscriber's demographic profile.For example, a television-programming provider may implement a programof voluntary logging of television viewing by a viewer, followed bytransmission and human processing to analyze the information containedin the log. In addition, a provider may utilize telephone, mail, orother types of surveys to inquire from random or selected viewers aboutthe viewers; viewing habits and requests their recollections regardingtheir viewing patterns. A provider may also utilize automated monitoringsystems that attempt to intercept television channel choices andchanges, record these events, and provide the recording to aclearinghouse or other facility for further processing.

The provider may enlist a ratings company to perform the monitoring andprocessing. For example, Nielsen Media Research (Nielsen Media Research,Inc., New York, N.Y.), Arbitron (Arbitron Inc., New York, N.Y.), andMeasureCast (MeasureCast, Inc., Portland, Oreg.) provide third-partymonitoring and processing capability for television, radio, and Internetcontent.

The Nielsen Media Research (Nielsen) Ratings are perhaps the best knownof the various third-party ratings services. Nielsen utilizes a varietyof conventional sampling methods to determine the number of viewerswatching a particular show. For example, in five thousand homes, Nielseninstalls a People Meter. The People Meter records viewing patterns fromtelevision sets, cable television set-top boxes, videocassetterecorders, satellite television set-top boxes, and other sources oftelevision programming. The People Meter records what content theparticular device is providing on an ongoing basis and periodicallytransmits this information to servers within a Nielsen facility. Nielsencombines the data uploaded from the People Meter with media content datato determine what programming and advertising a device displayed.Nielsen uses the combined data to provide a rating for each program andadvertisement. In conjunction with the People Meter, Nielsen alsoutilizes viewer diaries and surveys to gather information from a broaderspectrum of television viewers and to confirm the results generated bythe People Meter.

Arbitron, Inc. (Arbitron) is well known for providing radio broadcastratings. Arbitron compiles ratings by utilizing surveys. Arbitron alsoprovides television ratings based on various sampling techniques. Incooperation with Nielsen, Arbitron has developed a Portable People Meterto measure television ratings. The portable People Meter is apager-sized device, worn by a participant in a survey. The PortablePeople Meter records viewing by recording sounds encoded into eachbroadcast, which identify the program or advertisement. The surveyparticipant periodically plugs the Portable People Meter into arecharger, which also includes a communicator that uploads the data inthe Portable People Meter into a remote Arbitron server. The PortablePeople Meter may be a more accurate method of television ratings than aset-top box, such as the set-top box used by Nielsen. The PortablePeople Meter offers the advantage of capturing viewing outside the homeand of recognizing when the viewer is not within audible range of atelevision, and therefore, less likely to be viewing a particularprogram or advertisement.

As the use of the Internet increases, the distribution of programmingvia Internet channels becomes more important. MeasureCast, Inc.(MeasureCast) provides a ratings system for Internet media streaming.MeasureCast records the number of streams requested from a streamingserver and provides reports to programming providers and advertisersdetailing the popularity of particular streams. As is the case intraditional broadcast media, the more popular the stream, the higher theadvertising rate a broadcaster is able to charge.

Nielsen, Arbitron, and MeasureCast provide direct methods of measuringthe popularity of a program. Various indirect methods are also used todetermine the popularity of programming and the effectiveness ofadvertising. For example, advertising effectiveness is often measured interms of viewer attitudes and subsequent viewer actions, such aspurchases, inquiries, behavior changes, and other actions. Methods ofobtaining these indirect measures include: focus group tests,post-advertising surveys questioning whether an advertisement wasviewed, remembered and possible impact, and measures of productpurchases or other indirect results that may indicate whether or not anadvertising campaign has been successful.

Conventional methods for identifying desirable subscribers areinefficient and ineffective in identify specific subscribers or smallgroups of subscribers to which a content provider can direct resources.For example, conventional systems, such as the Nielsen and Arbitronmeters, rely on small samples, which may not be representative of thetarget market for a particular content provider. Conventional methodsfor identifying desirable subscribers also lack an efficient means formatching the demographics, content-access patterns, spending habits, andother attributes with specific subscribers on a large-scale basis.Therefore, subscribers are targeted as generalized groups, rather thanaccurately targeted as individuals or as members of small, homogenousgroups.

Also, surveys are expensive and highly dependent on identifyingindividuals that are of interest to the particular content providersponsoring the survey. Post-advertising results measurements suffer fromquestions of causality and external influences. Focus groups allowreasonably efficient low-volume viewer analysis, but statisticalanalysis requires an adequate number of participants and tightlycontrolled tests, a combination that may be difficult to achieve.

Also, because of comprehensive information about a subscriber isunavailable, it may be difficult or impossible in conventional systemsto determine a causal link between a particular viewing pattern orattribute and an action. A subscriber may show an interest in acategory, but the interest may not lead to an action. The contentprovider has no direct way of determining the causal link. For example,a subscriber may view many automobile programs or advertisements butnever purchase a new automobile. A different subscriber may purchase newautomobiles regularly yet never watch a program or advertisement devotedto automobiles. Establishing the causal link is of great value to thecontent provider.

SUMMARY

Exemplary embodiments provide systems and methods for utilizinginformation relating to a subscriber to identify the subscriber asdesirable. According to some of the embodiments, a computerized systemincludes a content-access information database, a subscriber attributedatabase, and a subscriber information database. The computerized systemmay also include a merge processor to merge information from the contentaccess information and subscriber attribute databases to create data inthe subscriber information database. The computerized system may alsoinclude a data analyzer electronically connected to the subscriberinformation database. The analyzer allows a content provider to accesssubscriber information to determine patterns of content access and tocorrelate these patterns with additional subscriber information.

According to exemplary embodiments, the subscriber attribute databaseincludes information associated with a subscriber that allows contentproviders to identify the subscriber as a desirable subscriber. Forexample, in some of the embodiments, the subscriber attribute databaseincludes a purchase history for the subscriber, such as a credit carddatabase. The purchase history may include purchases of the contentprovider's products and/or services and may include purchases ofproducts and/or services, which are complementary to or competitive withthe content provider's products and/or services. In other exemplaryembodiments, the subscriber attribute database includes aproperty-ownership database. In yet another exemplary embodiment, thesubscriber attribute database includes a survey result or questionnaireresponse database.

The data analyzer in an exemplary embodiment includes a tool used bycontent providers to identify desirable subscribers. The data analyzermay include a report creator, a multidimensional database, and/or adata-mining application. A data-mining application allows a user toderive new information or discover new patterns in datasets.

In an exemplary embodiment, the content provider or other user wishingto identify desirable subscribers receives viewing informationassociated with a subscriber, receives subscriber attributes, merges thereceived information into a single dataset, and then analyzes thedataset to identify the subscribers. The subscriber may be desirable asa consumer or for some other purpose of value to the provider performinganalysis of the data.

The viewing information may include programming and/or advertising data.The viewing information may also identify the time-of-day, durationand/or other distinguishing characteristics of the subscriber's viewingpatterns.

According to some of the embodiments includes a computer-readable mediumon which is encoded computer program code for utilizing information onrelating to a subscriber in order to identify the subscriber as adesirable subscriber.

According to some of the embodiments offers numerous advantages overconventional systems and methods for identifying desirable subscribers.In an exemplary embodiment, an analyst uses subscriber-specificinformation to identify desirable subscribers rather than data obtainedfrom small samples and then extrapolated to predict the behavior ofsimilar subscribers. By identifying groups of desirable subscribers, ananalyst is able to bring efficiency to the process of targetingconsumers or other subscribers. The process of targeting subscribers inan exemplary embodiment is more efficient that conventional approachesbecause rather than targeting large groups of subscribers in an attemptto deliver the content to the subset who may be desirable, the contentis either delivered to the same number of desirable subscribers for lesscost or to a larger number of desirable subscribers for the same cost.Less desirable subscribers do not receive the content, and consequentlycontent providers do not pay the cost for delivering content to the lessdesirable subscribers.

According to some of the embodiments offers further advantages tocontent providers and advertisers. In an exemplary embodiment, a contentprovider, advertiser, provider of goods and/or services, or other userhas the ability to compare subscriber purchases of complementary and/orcompetitive products. The ability to perform the comparison helps theprovider determine the relative strengths and/or weaknesses of theprovider's offering.

Also, in an exemplary embodiment, the user gains the ability tocorrelate actions, such as purchases, with content viewed by a specificsubscriber. For example, if a subscriber purchases a car within two daysof viewing a targeted advertising, the provider may be able to makecertain conclusions about the effectiveness of the advertisement.

Other systems, methods, and/or computer program products according toembodiments will be or become apparent to one with skill in the art uponreview of the following drawings and detailed description. It isintended that all such additional systems, methods, and/or computerprogram products be included within and protected by this descriptionand be within the scope of this invention.

DESCRIPTION OF THE DRAWINGS

The above and other embodiments, objects, uses, advantages, and novelfeatures are more clearly understood by reference to the followingDescription taken in connection with the accompanying figures, wherein:

FIG. 1 is a diagram of an exemplary operating environment according tosome of the embodiments.

FIG. 2 is a flowchart illustrating a process implemented to mergevarious data sources according to some of the embodiments.

FIG. 3A is a table illustrating various sources of programming andadvertising content available to a subscriber during a period of timeaccording to some of the embodiments.

FIG. 3B illustrates content displayed on a subscriber's televisionduring a period of time according to some of the embodiments.

FIG. 4 is a flowchart illustrating the process of merging the data shownin FIG. 3A to create the merged data shown in FIG. 3B according to someof the embodiments.

FIG. 5 is a table illustrating the programming viewed by the subscriberduring the period shown in FIGS. 3A, 3B, and 4 according to some of theembodiments.

FIG. 6 is a flowchart illustrating the process for identifying adesirable subscriber according to some of the embodiments.

FIG. 7A is a table illustrating a subscriber information databaseaccording to some of the embodiments.

FIGS. 7B and 7C are tables illustrating probability calculationsperformed on the data in the table in FIG. 7A according to some of theembodiments.

DESCRIPTION

This invention now will be described more fully hereinafter withreference to the accompanying drawings, in which exemplary embodimentsare shown. This invention may, however, be embodied in many differentforms and should not be construed as limited to the embodiments setforth herein. These embodiments are provided so that this disclosurewill be thorough and complete and will fully convey the scope of theinvention to those of ordinary skill in the art. Moreover, allstatements herein reciting embodiments of the invention, as well asspecific examples thereof, are intended to encompass both structural andfunctional equivalents thereof. Additionally, it is intended that suchequivalents include both currently known equivalents as well asequivalents developed in the future (i.e., any elements developed thatperform the same function, regardless of structure).

Thus, for example, it will be appreciated by those of ordinary skill inthe art that the diagrams, flowcharts, illustrations, and the likerepresent conceptual views or processes illustrating systems, methodsand computer program products embodying some of the embodiments of thisinvention. The functions of the various elements shown in the figuresmay be provided through the use of dedicated hardware as well ashardware capable of executing associated software. Similarly, anyswitches shown in the figures are conceptual only. Their function may becarried out through the operation of program logic, through dedicatedlogic, through the interaction of program control and dedicated logic,or even manually, the particular technique being selectable by theentity implementing some of the embodiments of this invention. Those ofordinary skill in the art further understand that the exemplaryhardware, software, processes, methods, and/or operating systemsdescribed herein are for illustrative purposes and, thus, are notintended to be limited to any particular named manufacturer.

Exemplary embodiments provide systems and methods for utilizinginformation relating to a subscriber to identify the subscriber asdesirable. According to some of the embodiments, a content-accessinformation database includes viewing information for a subscriber. Asubscriber attribute database includes additional data about thesubscriber. A merge processor combines this information, using a key,such as the subscriber's social security number, to create a subscriberinformation database. A data analyzer, such as a data-miningapplication, provides tools for searching the subscriber database or toidentify desirable subscribers. The data analyzer may provide a toolthat allows a content provider to correlate content-access information,such as television viewing habits, and other subscriber information toidentify desirable subscribers.

FIG. 1A is an exemplary operating environment for identifying one ormore desirable subscribers. As illustrated in FIG. 1A, a cableoperator's head-end facility 102 includes a merge processor 104, whichis in communication with a plurality of databases. These databasesinclude a local-content database 106, a subscriber-action database 112,and a national-content database 114. The merge processor 104 isprogrammed to receive and merge data from the two databases 112, 114.

The local-content database 106 includes information from the advertising108 and programming 110 databases. The advertising database 108including information related to local advertising produced and/orprovided by the cable operator or other local source. Likewise, theprogramming database 110 includes information related to locallyproduced and/or provided programming. The advertising database 108includes attributes of advertisements, such as, for example, theadvertiser, producer, brand, product type, length of the content, andother descriptive information. The programming database 110 includessimilar information related to programming, including the producer, typeof programming, length, rating, and other descriptive information. Thelocal-content 106, programming 108, and advertising 110 databasesinclude a date-time identifier, which indicates when a program oradvertisement has been provided. The date-time indicator provides a keyvalue for merging various databases with one another.

According to the exemplary embodiments of FIG. 1A, the cable operatorhead-end 102 also includes a national-content database 114. Thenational-content database 114 includes information from an advertisingdatabase 116 and a programming database 118. The information containedin each of these respective databases is similar to that contained inthe local advertising 108 and programming 110 databases. However, thecontent is produced for a national audience and subsequently provided tothe cable operator. The national-content 114, programming 118, andadvertising 116 databases also include a date-time identifier.

The cable operator head-end 102 also includes a subscriber-actiondatabase 112. The subscriber-action database 112 includes the actionstaken by subscribers while viewing television sets. For example,subscriber-action database 112 is in communication with cable network120. A processor (not shown) in cable network 120 receives anysubscriber actions transmitted via cable network 120 and inserts theactions as records in subscriber-action database 112. Also incommunication with cable network 120 is a set-top box 124, which isinstalled in a subscriber's home 122. Also located in subscriber's home122 is a television (TV) 126. As a subscriber 123 makes viewing choiceson TV 126 via set-top box 124, these choices or actions are transmittedvia a processor (not shown) in cable network 120 to subscriber-actiondatabase 112.

The subscriber-action database may include a clickstream database. Aclickstream database is common in Internet monitoring applications. Eachtime a web-browser user clicks on a link in a web page, a record of thatclick is stored in a conventional clickstream database. A database thatincludes similar information for television viewers is disclosed in U.S.Pat. No. 6,983,478, entitled “Method and System for Tracking NetworkUse” that was filed on Feb. 1, 2000, by Edward R. Grauch, et al., and ofwhich is hereby incorporated by reference. In the database described,each action taken by a television subscriber 123, such as “channel up”and “channel down” are stored in a database with a date-time stamp toallow tracking of the television subscriber's actions.

In the exemplary embodiments of FIG. 1A, a merge processor 104 receivesinformation from the local-content 106, national-content 114, andsubscriber-action 112, databases and merges the data based on date-timeattributes of the data. For example, a detail record in thesubscriber-action database 112 indicates that a subscriber's set-top box124 was tuned to channel 12, a National Broadcasting Company (NCB)affiliate. A record in the national-content database 114 indicates thatat the same point in time, NCB was broadcasting a Professional GolfAssociation (PGA) tournament. A record in the local-content database 106further indicates that the cable provider preempted the PGA tournamentto broadcast an infomercial for real estate investment strategy video.The merge process 104 receives information from each of these sourcesand determines that at the point in time of interest, the subscriber 123was watching the infomercial. The merge processor stores the resultantdata in the subscriber content-choice database 128. In some of theembodiments, the merge processor collects information from the variousdatabases rather than receiving it. For example, a program on the mergeprocessor 104 includes instructions for connecting to the variousdatabases and extracting data from each one.

According to other exemplary embodiments, the subscriber content-choicedatabase 128 includes merged information for a period of time and for aplurality of subscribers. For example, a program provider may wish totrack the popularity of a program for several thousand subscribers foran entire month. Another provider may be interested in analyzing theseasonal differences in subscriber viewing behaviors.

Although FIG. 1A illustrates a cable network having a two-way digitalcable network; various other cable network configurations may also beutilized. For example, the subscriber's home 122 may receive cableservice via a digital one-way cable system. In such a system, set-topbox 124 may communicate subscriber actions to subscriber-action databasethrough a modem and telephone connection periodically. In otherexemplary embodiments, subscriber 123 receives content through a digitalsubscriber line (DSL) from a DSL provider. In a DSL system, the set-topbox 124 is able to perform two-way communications and can thereforetransmit subscriber actions to subscriber-action database 112 directly.Further exemplary embodiments illustrating other content distributionnetworks (e.g., internet protocol networks) are described below inreference to FIG. 1B.

Although FIG. 1A illustrates the various databases and merge processor104 are located in the head-end facility 102, alternative exemplaryembodiments include other configurations, such as, the databases andmerge process 104 integrated within the set-top box 124 or as softwareresiding within a television network's facility (not shown). The datamay be captured and analyzed by programming and advertising producers ordistributors or may be utilized within a subscriber's set-top box 124 toprovide advanced services tailored to the subscriber 123.

The cable operator head-end facility 102 also includes a subscriberattribute database 130. The subscriber attribute database 130 includesinformation about the subscriber, including, for example, demographicinformation, a purchase history, and questionnaire responses. Thedemographic information may include the profession of the subscriber, aproperty ownership history of the subscriber, the age, income, maritalstatus, and other information useful in analyzing a subscriber'sbehavior. The data in the subscriber attribute database 130 may exist atvarious levels. For example, in some of the embodiments, the subscriberattribute database includes a purchase history of products, which arecomplementary or competitive to those of providers who analyze the data.

The cable operator head-end facility 102 also includes a subscriberinformation database 132. The merge processor 104 merges informationfrom the subscriber content choice 128 and subscriber attribute database130 to create records in the subscriber information database 132. Therecords in the databases are merged using a key attribute of asubscriber, such as the subscriber's social security number.

An analyzer 134 provides tools to a provider or other user to identifydesirable subscribers in the subscriber information database 132. Theanalyzer 134 may include various types of tools, including tools thatsearch the subscriber information database 132 automatically and toolsthat allow the user to perform manual searches. For example, theanalyzer 134 may include a simple report creator that allows the user tosearch the subscriber information database, perform filtering andsorting, and print the results. For example, in the illustratedexemplary embodiments, the analyzer 134 automatically creates reports136. The reports include summary and detailed information correlatingsubscriber content-access history and other attributes, such as apurchase history.

The analyzer 134 may include more complex tools such as an onlineanalytical process application or multidimensional database. In anexemplary embodiment, analyzer 134 includes a data-mining application. Adata-mining application allows a user to derive new information ordiscover new patterns in existing datasets. In another exemplaryembodiment, analyzer 134 includes a combination of these tools so that aprovider can make the best use of the data.

For example, a data-mining application on the analyzer 134 producesreports 136, illustrating influences and affinities within thesubscriber information database 132. Influences indicatecause-and-effect relationships between various data elements. Affinitiesdescribe groupings of data or data that occur under the samecircumstances. For example, a subscriber who accesses certain content(e.g., media content, Internet content, and other distributed content)may also be likely to purchase a certain brand of soft drink. Theaccessed content and the soft drink brand share an affinity or inherentsimilarity. The data-mining application provides additional reports 136,which detail anomalies in the data, such as a specific demographic grouppurchasing goods generally aimed at a completely different demographicgroup. For example, generally television viewers in demographic group 1tend to view nature shows almost exclusively. However, based on adata-mining report, a majority of these same television viewers watchShow A, which is aimed a science fiction show. Using this information,advertisers now know to advertise during both nature shows and duringShow A.

According to an exemplary embodiment, the various databases and mergeprocessor 104 are located in the head-end facility 102. In otherexemplary embodiments, the databases and merge processor 104 exist assoftware within the set-top box 124 or as software residing within atelevision network's facility (not shown). The data may be captured andanalyzed by programming and advertising producers or distributors or maybe utilized within a subscriber's set-top box 124 to provide advancedservices tailored to the subscriber.

FIG. 2 is a flowchart illustrating the general process the mergeprocessor (shown as reference numeral 104 in FIG. 1A) implements tocategorize and merge data from the various databases in an exemplaryembodiment. FIGS. 3-7 illustrate the process in greater detail.

Referring to FIG. 2, merge processor receives subscriber action datafrom the subscriber-action database (112) 202. Subscriber action datamay include data indicating that the subscriber 123 viewed an alternatedata source for a period of time. For example, the subscriber 123 mayview video from a VCR or DVD or other video source for a period of time.This video source supersedes both national and local-content in thesubscriber content-choice database 128.

The merge processor also receives data from the national-contentdatabase (114) 204. National-content data includes data describingmedia, such as programming and media, supplied by national providers.The merge processor next assigns a category or genre to thenational-content data 206. A genre is a specific type of category usedin relation to artistic compositions, and genre and category are usedinterchangeably herein. The merge processor (104) assigns categories tocontent based on attributes of the content. For example, a program has aname and a creation date. The name of the program is, “Wake ForestUniversity vs. Duke University Basketball Game,” and a creation dateequal to the current date. The merge processor (104) uses logic in acomputer program to determine that the program should be categorized asa “Live Sporting Event.” The merge processor (104) may assign multiplecategories to a single program, such as “Basketball,” “Sports,”“College-Related Programming,” or some other broad descriptive term.

The merge processor also receives data from the local-content database(106) 208. The merge processor (104) then assigns a category to thelocal-content data 210 in a manner similar to the process of assigning acategory to national-content data.

Once the merge processor has assigned a category to data in each of thecontent databases, the merge processor merges the categorized contentdata, national and local, with data from the subscriber-action database(112) 212 and creates records with the combined data in the subscribercontent-choice database (128) 214. Since the content data wascategorized prior to the merge process, the data in the subscribercontent-choice database 214 retains the assigned categories. Therefore,data in the subscriber content-choice database 214 can be sorted,filtered, reported, and used for various other processes, which utilizegroupings of the data.

The subscriber content-choice database 128 may be implemented in variousways. For example, the database 128 may simply be a number of tables ina relational database. To simplify the process of querying the data, thedatabase may include an online analytical processing tool, such as amultidimensional database.

FIG. 3A illustrates the sources of programming and advertising contentavailable to the subscriber 123 while the set-top box 124 is tuned to asingle channel. FIG. 3B illustrates the content displayed on the TV.FIG. 4 is a flowchart illustrating the process of merging the variouscontent types shown in FIG. 3A to determine the content displayed on aparticular channel.

FIG. 3A includes a Content Type column 302. The various content typesdisplayed in the Content Type column 302 are show in relation to Time304. Time 304 in FIG. 3A is divided into hour 306 and quarter-hour 308segments. FIG. 3A represents a simplistic scenario in which set-top box124 is tuned into a single channel. Therefore, the Content Type 302column includes five types of content: National Programming 310,National Advertising 312, Local Programming 314, Local Advertising 316,and Other Video Source 318. In order to present a simplified view of theavailable content types during the period, several content typesoverlap, when in reality, they would actually occur in series. Forexample, National Programming 310 and National Advertising 312 do notoccur at the same time, but it is likely that programming andadvertising both would be broadcast for at least some period of timeduring the fifteen minute periods of overlap shown in FIG. 3A. Forexample, during a television program provided by a broadcast network, atwo or three-minute break occurs approximately every fifteen minutes.Therefore, a fifteen-minute period in which a three-minute break occurswill include twelve minutes of programming and three minutes ofadvertising.

As shown in FIG. 3A, multiple types of content may be provided duringany period of time. The fact that the content is provided does notindicate that it is available on the set-top box (124) or that thesubscriber 123 is viewing the content. For example, in the embodimentshown, the cable provider provided National Programming 310 continuouslythroughout the period. The provider provided National Advertising 312approximately every 15 minutes during the same period. Also, the cableprovider provided Local Programming 314 from 1:00 until 2:30, and LocalAdvertising 316 approximately every 15 minutes during that period. Thecable provider subsequently provided Local Advertising 315 during theperiod beginning at 5:15. Also during the period shown in FIG. 3A, thesubscriber 123 viewed input from the Other Video Source 318, e.g., VCRor DVD, from 2:30 until 4:15.

FIG. 4 illustrates exemplary process for determining which programmingis displayed on the subscriber's television during any specific periodof time and inserting that data into the subscriber content-choicedatabase 128 if the subscriber 123 is viewing that channel. Althoughvarious sources of content, such as a cable TV channel or a DVD movie,may be available to the subscriber (123) during any period of time, thesubscriber (123) generally views only one source of programming oradvertising at any one time. In addition, a content provider, such as acable operator, makes determinations regarding which content will beavailable via a communications channel.

According to an exemplary embodiment, a computer program executing onmerge processor (104) processes the potentially viewable data sources asa hierarchy. The program first determines, using information in thesubscriber-action database (112), whether the subscriber (123) wasviewing another video source, such as a VCR or DVD 402. If so, theprogram inserts data describing the other video source 404 into thesubscriber content-choice database (128), and the process ends 416.

If the subscriber (123) was not viewing an alternate source of video andwas tuned to a particular channel, then the subscriber (123) was viewingthe content provided by the cable operator on that channel. To determinewhat content was provided by the cable provider, the program executingon the merge processor (104) determines whether the cable provider wasproviding local programming or advertising during the period of time 406by accessing the local-content database (106). If so, the programinserts data describing the local programming or advertising 408 intothe subscriber content-choice database (128), and the process ends. Ifthe cable provider was not providing local programming or advertising,the program determines whether or not the provider was providingnational programming or advertising 410 by accessing thenational-content database (114). If so, the program inserts datadescribing the national programming or advertising 412 into thesubscriber content-choice database (128), and the process ends 416.

If the program determines that the subscriber 123 was not viewinganother video source and the provider was providing no content, theprogram either inserts a record in the subscriber content-choicedatabase 128 indicating that no content was available during thespecific period of time or inserts no data at all 416. For example, ifTV 126 is left on after a broadcaster ends broadcasting for the rest ofthe day, no content is available after the broadcaster ceasesbroadcasting, so either a record indicating the lack of content isinserted or no data is inserted.

It is important to note that in an exemplary embodiment, the processillustrated in FIG. 4 is repeated for each period of time that is ofinterest for analyzing the data. The result of the process is pluralityof records describing a subscriber's viewing patterns during a period oftime. In some of the embodiments, the subscriber content-choice database(128) includes data from a plurality of subscribers as well. Thedatabases and processor (104) in such an embodiment are configuredappropriately to process the anticipated volume of data.

According to the exemplary embodiments shown in FIGS. 3A and 3B, theprocess is repeated for each quarter hour. In other embodiments, thetime period may be divided into small increments, such as tenth-of-asecond increments.

FIG. 3B illustrates the result of merging the data records shown in FIG.3A using the process illustrated in FIG. 4. As in FIG. 3A, FIG. 3B is asimplistic view of this data, including the Content Type 302 and thevarious slices of time 304, 306, 308. In the table shown in FIG. 3B, theContent Type column 302 includes only a Programming 320 and anAdvertising 322 row.

As shown in FIG. 3A, during the period from 1:00 until 2:30, the cableprovider provides local programming and advertising 312, 314. Theprocess of FIG. 4 determined that the subscriber 123 was viewing noother video source 318, and therefore, the program inserts data into thesubscriber content-choice database 128 related to local programming andadvertising 320, 322. During the period beginning at 2:30 and ending at4:15, the subscriber 123 viewed video from another source 318.Therefore, the program inserts data related to the other source for thistime period. During the period from 4:15 until 5:15, the providerprovided national programming and advertising with the exception of theperiod from 5:16 until 5:30, during which local advertising as provided.The program inserts this data into the subscriber content-choicedatabase.

FIG. 5 is a table illustrating exemplary programming that the subscriber123 viewed during the period shown in FIGS. 3A and 3B. As with FIGS. 3Aand 3B, the table includes a Time section 502 and a Content section 504.The Time section 502 is divided into hour and quarter-hour segments.

According to FIGS. 3A and 3B, between 1:00 and 2:30, the subscriber 123viewed local programming and advertising. By accessing the local-contentdatabase (106), the merge processor (104) determines that the localprogramming consisted of a NCAA (National Collegiate AthleticAssociation) basketball game and local advertising 506.

According to FIGS. 3A and 3B, during the period from 2:30 until 4:15,the subscriber (123) viewed a DVD 508. The merge processor (104)determines that the DVD was a science fiction DVD by extracting datafrom the subscriber-action database (112).

And according to FIGS. 3A and 3B, between 4:15 and 5:15, the subscriber(123) viewed national content and advertising, with the exception of theperiod between 5:15 and 5:30 during which the cable operator inserted alocal advertisement segment in the content stream in place of thenational content 510. By accessing the national-content database (114),the merge processor (104) determines that the national content viewed bythe subscriber (123) was an NBA (National Basketball Association)basketball game.

According to exemplary embodiments, an analyst evaluates the data shownin FIG. 5 to determine preferences and viewing habits of the subscriber(123). In some of the embodiments, the analyst is a computer programexecuting on a processor (not shown). The analyst also attempts toextrapolate the data in order to project purchase habits of thesubscriber 123. In order to evaluate the data shown in FIG. 5, theanalyst begins by assigning a category or genre to the programming. Forexample, during the period between 1:00 and 2:30, the subscriber 123viewed a NCAA basketball game 506. An analyst would assign various typesand levels of categories to the game, such as basketball, collegeathletics (type of program), college name, and conference. The analystmay also note that sometime between 2:15 and 2:30, a PGA golf tournamentbegan, and the subscriber 123 started a DVD movie. This might indicatethat the subscriber 123 did not enjoy watching golf on TV. During thesame period, the subscriber 123 also watched several advertisements. Theanalyst categorizes these as well. The analyst repeats the process ofcategorization of programming and advertising for the remainder of thedata 508, 510.

By categorizing content using multiple category types and multiplelevels, the analyst is able to provide an abundance of information toprogramming and advertising producers, and providers, as well as to theproduct owners and manufacturers who pay to have the ads produced anddistributed. Categorization in this manner also provides the analystwith multiple perspectives from which to analyze the data.

According to other exemplary embodiments, the analyst may look forpatterns or correlations between multiple programs and advertisements orbetween categories of multiple programs and advertisements. Incorrelating data, the analyst is seeking causal, complementary,parallel, or reciprocal relations between various occurrences of data.For example, in the embodiment shown in FIG. 5, the subscriber 123viewed a basketball game, a science fiction movie, and anotherbasketball game. An analyst may correlate this data and find that thesubscriber 123 generally watches primarily sports broadcasts, andotherwise watches content from video sources in the home. The analystmay also perform a probably analysis to determine the likelihood that asubscriber 123 will watch a particular category or genre of show ifpresented with the opportunity.

Although only a brief period of time is shown in the Figures, thesubscriber content-choice database includes data recorded continuallyover many days. By analyzing various days and time periods, an analystcan determine a subscriber's time-of-day viewing patterns as well as thesubscriber's patterns of viewing duration. For example, an analyst maydetermine whether the subscriber 123 tends to view the entirety of aprogram or of an advertisement.

Determining the duration of viewing of advertisements is important toadvertisers. If a subscriber 123 initially views an entire advertisementbut subsequently, views only a small portion of the advertisement, thenthe advertiser may need to reschedule the advertisement so that it runsless frequently, or replace the advertisement altogether. Also, ifsubscribers viewing a particular category of programming generally viewads in their entirety, but other viewers do not, the advertiser may wantto focus resources on presenting the advertisement to these viewers.

Beyond analyzing ads in general, advertisers may also desire informationrelated to specific ads or even of a competitor's ads. Using theinformation, the advertiser may be able to determine the relativestrengths and weaknesses of the advertiser's own strategy versus acompetitor's strategy.

In some of the embodiments, various indirect methods are also used todetermine the popularity of programming and the effectiveness ofadvertising. For example, advertising effectiveness is often measured interms of viewer attitudes and subsequent viewer actions, such aspurchases, inquiries, behavior changes, and other actions. Methods ofobtaining these indirect measures include: focus group tests,post-advertising surveys, questioning whether an advertisement wasviewed, remembered and possible impact, and measures of productpurchases or other indirect results that may indicate whether or not anadvertising campaign has been successful. In an exemplary embodiment,additional databases store the data derived through these indirectmethods. The merge processor 104 combines this data with the data in thesubscriber content-choice database 128 to provide additional informationto analysts and content providers.

FIG. 6 is a flowchart illustrating an exemplary process for identifyinga desirable subscriber after data has been merged into the subscriberinformation database 132. By using the process illustrated in FIG. 6, anadvertiser, programmer, distributor, or other content provideridentifies subscribers to target. A content provider may carry out theprocess illustrated in FIG. 6 manually, allow the analyzer 134 to carryout the process automatically, or may use some combination of the two.

According to an exemplary automated process, the analyzer 134 determinesthe relevant criteria for determining which subscribers should receive atargeted advertisement 602. For example, a product may be associatedwith a specific demographic group. Also, past purchasers of a productmay be much more likely to purchase the same product in the future thanpeople who have never before purchased the product.

The advertiser next identifies the relevant information in thesubscriber information database (132) 604. For example, if the productis associated with a demographic group, and specific content, such as atelevision program, is also associated with the demographic group, theanalyzer 134 identifies the viewing history for a subscriber as therelevant information in the database 134. If purchases of the productrelate to past purchases, the analyzer 134 identifies a purchase historyin the database 132 as relevant.

The analyzer 134 extracts the relevant data from the database (132) 606.Extracting the information may include creating a report, spreadsheet,or other format, which can be used by the analyzer 134 or a contentprovider to examine the data to identify desirable subscribers.

In the exemplary process shown in FIG. 6, the analyzer 134 nextdetermines the probability that a subscriber will purchase Product Abased on the first attribute 608 and on the second attribute 610. FIG.7A and 7B illustrate an example of how the probability calculation maybe performed.

The table of FIG. 7A illustrates a data extract 702 from a subscriberinformation database 132, resulting from the extract step 606 in FIG. 6.In the exemplary embodiment shown, the extract 702 includes 3 columns,viewing history 704, city of residence 706, and a flag denoting whetheror not the subscriber has purchased product A 708. The advertiser usesthe data from the extract 702 to determine the probability that asubscriber with a particular viewing history 704 or city of residence706 has purchased product A 708.

FIGS. 7B and 7C are tables illustrating the result of the probabilitycalculations by viewing history and city respectively. The table 710shown in FIG. 7B includes two columns: Viewing History 712 andProbability expressed as a percentage 714. Probability 714 is theprobability, based on the data in the extract 702, that a subscriber whoviewed the program shown in the Viewing History column 712 will purchaseProduct A. For example, in the data extract 702, two out of threesubscribers who watched Show 1 purchased Product A. Therefore, absentany other influences, the probability that a subscriber who views Show 1will purchase Product A is 66.7%.

FIG. 7C includes a table 720, illustrating a similar calculation forcity of residence. The table 720 includes a City column 722 and aProbability column 724. Based on the data shown, the probability that asubscriber who resides in city A will purchase Product A is 66.7% basedon the data extract 702 shown n in FIG. 7A.

Referring again to FIG. 6, once the advertiser has determined theprobability for each of the attributes, the advertiser determines whichsubscribers are to be targeted 612. Since the advertiser believes thatit is best to target subscribers who have purchased Product A in thepast, the advertiser targets subscribers in the following order: (1)views of Show 1 and/or residents of city A (tie), (2) viewers of Show 2,(3) residents of city B or C (tie), and (4) viewers of Show 3.

The example illustrated by FIGS. 7A-C relies on only two variables. Infurther exemplary embodiments, a user (e.g., an advertiser) relies onmany additional variables to determine the most effective and efficientuse of advertising budget.

Exemplary embodiments of this invention provide great value to contentproviders. As a result, content providers are willing to pay for theoutputs derived from the various reports and analysis. The contentproviders may be billed a flat subscription-type rate for access to allinformation received, or they may pay for each report and/or analysisthat they request.

Further exemplary embodiments include a computer-readable medium, havingcomputer-readable instructions for assigning a category and merging thesubscriber action and media-content information. Another exemplaryembodiment includes computer-readable instructions for correlatingmultiple subscriber actions occurring over a period of time, such as anonline analytical processing application.

A computer-readable medium includes an electronic, optical, magnetic, orother storage or transmission device capable of providing a processor,such as the processor in a web server, with computer-readableinstructions. Examples of such media include, but are not limited to, afloppy disk, CD-ROM, magnetic disk, memory chip, or any other mediumfrom which a computer processor can read. Also various other forms ofcomputer-readable media may transmit or carry instructions to acomputer, including a router, private or public network, or othertransmission device or channel.

While several exemplary implementations of embodiments of this inventionare described herein, various modifications and alternate embodimentswill occur to those of ordinary skill in the art. For example, thearchitecture and programming of the system may be modified. Or, avariety of different manufacturers' servers, set top boxes (includingother media delivery devices), and/or databases may be configured inorder to implement exemplary embodiment of this invention. Further, theexemplary identification codes and allocated sizes show in the tablesand described herein may also be greatly modified. Accordingly, thisinvention is intended to include those other variations, modifications,and alternate embodiments that adhere to the spirit and scope of thisinvention.

1. A method for targeting an advertisement, comprising: receivingclickstream data describing at least subscriber actions; receivingcontent information describing what content is available; receiving acriterion for determining which subscribers should receive a targetedadvertisement; merging the clickstream data with the content informationto generate data describing at least one event timeline; and targetingat least one subscriber to receive the advertisement when the datadescribing the at least one event timeline matches the at least onecriterion.
 2. The method of claim 1, further comprising matching thedata describing the at least one event timeline with the criterion. 3.The method of claim 1, further comprising targeting a group ofsubscribers to receive the advertisement when the data describing the atleast one event timeline matches the at least one criterion.
 4. Themethod of claim 1, further comprising comparing purchasing data to thedata describing the at least one event timeline.
 5. The method of claim1, further comprising merging the clickstream data with the contentinformation and with a subscriber key.
 6. The method of claim 1, furthercomprising defining the criterion as an affinity between the contentinformation and the advertisement.
 7. The method of claim 1, furthercomprising categorizing the content information.
 8. The method of claim1, further comprising categorizing the content information prior to themerging.
 9. A system for targeting an advertisement, comprising: aprocessor executing code stored in memory that causes the processor to:receive clickstream data describing at least subscriber actions; receivecontent information describing what content is available; receive acriterion for determining which subscribers should receive a targetedadvertisement; merge the clickstream data with the content informationto generate data describing at least one event timeline; and target atleast one subscriber to receive the advertisement when the datadescribing the at least one event timeline matches the at least onecriterion.
 10. The system according to claim 9, further comprising codethat causes the processor to match the data describing the at least oneevent timeline with the criterion.
 11. The system according to claim 9,further comprising code that causes the processor to target a group ofsubscribers to receive the advertisement when the data describing the atleast one event timeline matches the at least one criterion.
 12. Thesystem according to claim 9, further comprising code that causes theprocessor to compare purchasing data to the data describing the at leastone event timeline.
 13. The system according to claim 9, furthercomprising code that causes the processor to merge the clickstream datawith the content information and with a subscriber key.
 14. The systemaccording to claim 9, further comprising code that causes the processorto define the criterion as an affinity between the content informationand the advertisement.
 15. The system according to claim 9, furthercomprising code that causes the processor to categorize the contentinformation.
 16. The system according to claim 9, further comprisingcode that causes the processor to categorize the content informationprior to the merging.
 17. A computer-readable storage medium on which isencoded processor executable instructions for performing a method, themethod comprising: receiving clickstream data describing at leastsubscriber actions; receiving content information describing whatcontent is available; receiving a criterion for determining whichsubscribers should receive a targeted advertisement; merging theclickstream data with the content information to generate datadescribing at least one event timeline; and targeting at least onesubscriber to receive the advertisement when the data describing the atleast one event timeline matches the at least one criterion.
 18. Thecomputer-readable storage medium of claim 17, further comprisinginstructions for matching the data describing the at least one eventtimeline with the criterion.
 19. The computer-readable storage medium ofclaim 17, further comprising instructions for targeting a group ofsubscribers to receive the advertisement when the data describing the atleast one event timeline matches the at least one criterion.
 20. Thecomputer-readable storage medium of claim 17, further comprisinginstructions for comparing purchasing data to the data describing the atleast one event timeline.